Visual Analysis of the Air Pollution Problem in Hong Kong Huamin Qu, Wing-Yi Chan , Anbang Xu, Kai-Lun Chung, Kai-Hon Lau, and Ping Guo The Hong Kong University of Science and Technology Beijing Normal University IEEE Visualization 2007 October 30, 2007, Sacramento, CA, USA
Outline • Introduction • Background and Motivation • Uniqueness of Weather Data • Challenges • Related Work • Visualization Modules • Experimental Results • Conclusions and Future Work
Background: Hong Kong
Background: Air Pollution Problem in Hong Kong • Hong Kong air quality is decreasing tremendously • Air pollution problem becomes one of the biggest social issues • Causes are still unknown • Hypotheses are proposed without any formal proof
Motivation • Institute for the Environment of the Hong Kong University of Science and Technology (HKUST) • Maintain a comprehensive environmental database on Hong Kong and surrounding regions • Found correlations with classical analysis methods • Could not obtain convincing results for high-level correlations with mathematical techniques • Demanded visualization techniques for analysis
Uniqueness of Weather Data Weather data attributes • Time-series 1. Precipitation • Recorded hourly 2. Wind Direction 3. Air Temperature • Span >10 years 4. Wind Speed • Geographic information 5. Dew Point 6. Relative Humidity • Inherit from monitoring station 7. Sea Level Pressure • Multi-dimensional 8. Respirable Suspended Particulates (RSP) • Have typically >10 attributes 9. Nitrogen Dioxide (NO 2 ) 10. Sulphur Dioxide (SO 2 ) • Important vector field 11. Ozone (O 3 ) • Formed by wind speed and 12. Carbon monoxide (CO) 13. Solar Radiation wind direction 14. Air Pollution Index (API) 15. Contributed Pollutant to API
Challenges • People are familiar with the existing tools to represent wind profile • Polar coordinates and orientated arrows are commonly used • This constraints the design of visualization tool • Data are of large size and high dimensionality • Data are both multivariate and time-series • It should provide an intuitive way to compare the data across time and stations
Outline • Introduction • Related Work • System Overview • Experimental Results • Conclusions and Future Work
Related Work • Weather data visualization is rarely considered as a standalone problem • Uniqueness of weather data is sometimes overlooked • Vector values • Time-series nature
Related Work (cont.) • Scientific visualization approaches [Treinish’00] • Show the weather condition rather than the underlying data for analysis propose • Nonphotorealistic brushes and natural textures [Healey‘04, Tang’06] • Generate effective results but is scalable to only 3 attributes • General multivariate applications [Luo‘03, Guo’06, Wilinson‘06] • Do not addressed the important wind factor • Cannot be directly used for air quality analysis [Treinish’00] [Healey’04] [Guo’06]
Outline • Introduction • Related Work • Visualization Modules • Experimental Results • Conclusions and Future Work
Visualization Modules • Polar system with embedded circular pixel bars • Tailored parallel coordinates with S-shape axis • Weighted complete graph
Visualization Module 1: Polar System • Has been heavily applied by domain scientists • Allows query by wind speed and direction • Uses area-preserving mapping on distances • Points are not over- compressed in the center Not Preserved Area Preserved
Polar System with Embedded Circular Pixel Bar • Users select a sector to plot the inside-sector data of certain wind direction and speed • A complement plot of outside-sector data is blended underneath to compare against overall distribution
Advantages of Embedded Circular Plots • The corresponding wind direction and speed is obvious for rapid comparisons between sectors • Overall patterns are preserved in circular plots • Users may perform accurate numerical analysis with the supplement traditional layout
Visualization Module 2: Parallel Coordinates with S-Shape Axis • Vertical axis is not good at encoding directions • S-shape axis is introduced • Similar to polar system • Stands out among all axes to attract attention Traditional layout Circular layout S-style layout (not intuitive) (many overlapping)
Tailored Parallel Coordinates • Scatter-plots are provided for detailed analysis
Visualization Module 3: Weighted Complete Graph • Each node represents one data dimension • Distance between nodes encodes their correlations • Calculated with standard correlation coefficient • Rendered by the LinLog energy model with the Barnes-Hut algorithm C A B not really correlated correlated
Weighted Complete Graph: Encoding Scheme • Size of a node encodes accumulated correlations between this node and all other nodes • Bigger nodes may have strong relationship with other nodes • Edges are included if the correlation is above a threshold to reinforce the encoding of correlation • Width: Absolute value of correlation coefficient • Color: Positive / negative correlations
Weighted Complete Graph for Ordering Parallel Axes • Helps explore overall relationship among dimensions • Guides ordering of axes for parallel coordinates (guided) (random)
Outline • Introduction • Related Work • Visualization Modules • Experimental Results • Correlation Detection • Similarities and Differences • Time-Series Trend • Discussion • Conclusions and Future Work
Experiment 1: Correlation Detection Color = API (Air Pollution Index) solar radiation sulphur dioxide SO 2 ozone O 3 • Sector of high RSP (Respirable Suspended Particulates) is selected • RSP is correlated with SO 2 and O 3 but not solar radiation • Known : API (Air Pollution Index) is correlated with O 3 (red pixels) but not SO 2 • Unknown : A blue cluster appears behind a green one for SO 2 and O 3 plots
Experiment 2: Similarities and Differences • External pollution from the factories on the Pearl River Delta (northwest of Hong Kong) is generally believed to be the main source • Local pollution is often ignored • Power plants • Vehicles and vessels (northwest) HK Pearl River Delta
Comparing 9 Districts in Hong Kong • High SO 2 for most stations: • Strong wind • Northwest wind • External Sources • High SO 2 for Kwai Chung: • All wind speed • Southwest wind • Internal sources likely due to cargo ships at Kwai Tsing Container Terminals 9 stations of 3 years data Color = amount of SO 2
Detailed Comparisons with Sectors • Sectors with high SO 2 are selected for further studies • Kwai Chung data generally shows a higher API value than Tung Chung data • But SO 2 should not contribute much to API • Local pollution is dominating in Kwai Chung
Experiment 3: Time-Series Trend • Weighted complete graphs for 3-year Tung Chung data are generated • More correlated attributes are placed closer in the parallel coordinates display
Time-Series Trend with Parallel Coordinates • Polar system is applied for filtering • A time axis is added, with colors also denoting the time • 2004 and 2005 plots are more similar • In 2006 plot, temperature varies dramatically
Discussion: Using 3 Modules As A Whole • Use weighted complete graph for spotting correlated attributes and deciding the order of axes in parallel coordinates • Observe general correlation with parallel coordinates • Study specific relationships among 3 attributes for a subset of data using polar system with embedded pixel bars
Discussion: Feedback from Domain Scientists • Very positive feedback received from the domain scientists • Polar system with embedded pixel bar • Offers easy navigation to explore the data interactively • Tailored parallel coordinates • Show general relationships in a qualitative way • Use S-shape axis to encode directions intuitively • Supply scatter-plots for accurate analysis • Weighted complete graph • Provides correlation overview that is useful for initiating analysis • Aids axis order selection in parallel coordinates for clearer results
Outline • Introduction • Related Work • Visualization Modules • Experimental Results • Conclusions and Future Work
Conclusions • Comprehensive System • The first attempt designed for air quality analysis • Novel Techniques • Polar system with circular pixel bars: scalar + vector • Enhanced parallel coordinates: vector + time axes • Weighted complete graph: parallel axis order selection • Significant Application • Analyzed the air pollution problem of Hong Kong • Revealed known findings effectively • Detected unknown patterns by domain scientists
Future Work • Continue as a long-term project with the Institute for the Environment of HKUST • Make the visualization system available to the public on the Web • Incorporate new datasets for further exploration • Add animations and other visual aids for more revealing results
Acknowledgments Dr. Zibin Yuan at the Institute for the Environment of the Hong Kong University of Science and Technology
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